Syed Jawad Hussain Shahzad

Relationship between developed, European and South Asian stock markets: a multivariate analysis

Purpose n n n n nThe assessment of interdependence between stock markets is an important aspect of international portfolio management. The purpose of this paper is to examine and highlight the diversification potential of South Asian stock markets vis-a-vis developed and European stock markets. n n n n nDesign/methodology/approach n n n n nThe developed stocks markets include USA and UK, and South Asian stock markets include India, Pakistan and Sri Lanka while DJ STOXX 600 index is used to represent the European stock markets. Monthly data are used to examine long-run relationship through ARDL bound testing approach and estimates are obtained using DLOS. Short-term dynamics are captured through vector error correction-based Granger causality. n n n n nFindings n n n n nSouth Asian stock markets are closely linked with each other; similarly, developed/European markets are interlinked. US stock market not only impacts European stock markets, it also Granger cause South Asian stock markets. The findings suggest increase in comovement of South Asian stock markets with the global markets after financial crises of 2007-2008. n n n n nPractical implications n n n n nThe diversification benefits of South Asian stock markets for international investors are still evident due to their low relationship (in both long and short run) with developed/European stock markets. n n n n nOriginality/value n n n n nGiven the emergence of South Asian stock markets, new insight on their relationship with developed stock markets can provide interesting findings for international portfolio diversification. The South Asian equity markets are an important source of investment because of their immense growth and weak correlation with international markets.

CAPM estimates: Can data frequency and time period lend a hand?

This study is based on positivism research philosophy and utilizes deductive approach. The study uses a dataset of 117 firms listed on KSE-100 Index from 2005 to 2012 to analyze the predictability of capital asset pricing model (CAPM) under different data frequencies and time frames. Six months daily data, in contradiction to the recommended five years monthly data, provides the best estimates. However, the performance of model can be regarded poor as it only explains 7.39% difference in returns.